Data Cleansing - Lessons Learned and Today's Solutions

History provides plenty of examples of the consequences of relying on flawed data in business. From the Thalidomide Drug Disaster, the Challenger Space Shuttle Disaster, and the Subprime Mortgage Crisis, each catastrophic example shares one thing in common… reliance upon flawed data. 

The data you rely on has daily implications for your bottom line, whether personal or business. Whatever business you may be involved in, data is crucial to moving it forward and staying competitive.  

Flawed Data - Everyday Business: 

Far too many business executives assume they are operating based on clean data, and failure to consider otherwise can negatively impact everyday business operations. 

• An inaccurate understanding of market opportunity negatively affects the allocation of resources and investments in markets, the ability to respond appropriately to market dynamics, and an inadequate understanding of pricing strategies.   
• Inaccurate financial data leads to missed revenue projections, errant financial statements, possible issues with the IRS, and impacts strategic and operational decisions. 
• Erroneous procurement data impacts forecasting, inventory management, and negotiations with suppliers, to name a few. Each of these, in turn, directly impacts overall business health, profitability, and competitiveness within the market. 
  
  

Businesses must prioritize accurate data collection and entry, provide data entry training, and implement systems to reduce and identify human errors and other issues with their data. Ongoing data monitoring for potential anomalies is required to quickly identify and address problems and enhance overall efficiency, boosting confidence in your decisions and ability to adapt rapidly and more effectively to changing market trends.  

Clean Data – Data Categorization – Why? 

Information should be categorized logically and systematically, enhance search and discovery capabilities, and ultimately a seamless and intuitive customer experience. Furthermore, your categorization mapping should lead to more effective inventory management and promote consistency across business systems.  

Properly categorized data lays the foundation for accurate and meaningful analytics, ensuring that analysis is based on relevant information and allowing data analysts to organize and structure data in a way that aligns with the specific objectives of the analysis. This categorization helps filter out the noise, enabling a more accurate and reliable understanding of the data and procurement processes.  

By investing resources into organizing product data, businesses can gain a competitive advantage, increase operational efficiency, and deliver a better overall customer experience.  

Clean Data – Data Categorization – How? 

The first step to achieving clean data is determining and implementing appropriate categories for your data. Appropriateness depends on your specific context and goals for your data analysis. Here are some steps to help: 

1. Identify critical characteristics that define your data and domain. 

1. Identify natural groupings. 

1. Validate your categories by applying them to a data sample, assess fit, and refine. 

1. Communicate clear guidelines to ensure consistent application across the business. 

1. Regularly review and update to accommodate new data and changing requirements. 

Clean Data – Data Cleansing – How? 

After establishing the categories, it is time to clean the data. Cleaning data involves identifying and correcting errors, inconsistencies, and inaccuracies to ensure quality and reliability. 

1. Review data to ensure understanding of each variable in the data set. 

1. Identify any missing values and input or exclude them.  

1. While we don’t want inflated numbers, it is also critical not to under-document numbers. Identify duplicate records and remove them. 

1. Identify anomalies and correct or remove them from the dataset.  

1. Determine and use consistent formatting for dates, numeric, categorical values, etc. 

1. Check for spelling errors, inconsistent capitalization, or representations of the same category that deviate from one another for your categorical variables.  

1. Standardize supplier names for consistency.  

1. Normalize numerical variables, scaling those with different units of measure to a standard. 

1. Multiple-sourced data must be combined and reconciled to create a unified and comprehensive dataset, requiring mapping, aligning fields, and ensuring compatibility. 

1. Convert raw data into a format suitable for analysis. Conversions may include aggregating, calculating derived metrics, and creating new variables necessary for analysis. 

1. Document process for transparency purposes and reproducibility.  

1. Use known benchmarks to validate the accuracy of your data and address inaccuracies. 

1. Run a simple analysis to ensure usability. Test your assumptions, perform a data quality check, and validate that the results meet expectations. 

1. Like categorizations, data cleaning is an iterative process. You will likely need to revisit and tweak some of the steps to ensure you can analyze as you need to. Cleaning is an ongoing process, completed each time you add new data. 

How can ProcureVue^TM help you clean? 

Categorizing, cleaning, analyzing, and interpreting data is our wheelhouse. It’s what we do. While the sheer prospect of undertaking cleaning data is overwhelming to many in procurement, our ProcureVue^TM proprietary systems and processes allow us to provide you with what many companies have spent years trying to achieve in a matter of weeks.  

We are happy to assist you with simply categorizing and cleaning your data in preparation for your data analytics team to utilize it more efficiently; however, most of our clients also take advantage of our in-depth analytics capabilities. Once cleaned, our process enriches your data with trusted industry indices and other related data sources in ProcureVue^TM cost builds, allowing us to gain a true ‘should-cost’ across the enterprise. Our DataVue^TM system transforms your defective data into easily digestible visualized insights and provides a list of quick-hitting items for immediate engagement. Many clients choose monthly monitoring to maintain clean data, track successes, and identify additional market opportunities.  

If clean data and in-depth analysis at the macro and micro levels could benefit you, then ProcureVue^TM would love to partner with you to improve your competitive edge. Remember, we can deliver in weeks. When you need accurate value-added insights quickly, think ProcureVue^TM. And why would you ever want them any other way? 

This blog post is a condensed version of our whitepaper, which you can view below: